Computer Vision and Intelligent Machines track

Education directors :

  • VMI track and initial training: Laurent WENDLING and Camille KURTZ
  • Apprenticeship – from M2: Nicolas LOMENIE and Sylvain LOBRY

 

 

Overview

The VMI track offers students an in-depth Master’s degree in Computer Science in fields related to image processing, visual recognition, computer vision, and machine learning, which are current branches of artificial intelligence (AI). The program combines coherent coursework, from the fundamentals of the discipline to the most advanced techniques for the design and development of intelligent machines. The second year (M2) can be taken either as an initial program (traditional teaching followed by a final internship) or as an apprenticeship (alternating between teaching and professional work).

 

Courses are taught by professors and researchers from the Université Paris Cité (particularly members of the LIPADE lab) and other institutions (Institut Pasteur, Institut National de l’Information Géographique et Forêt – IGN, etc.), as well as by industry professionals. The program covers all aspects of modern computer science, from practical to fundamental aspects, leading to careers in computer engineering (R&D), basic or applied research, and higher education.

Objectives

Develop advanced expertise in imaging and computer vision

→ Acquire in-depth skills in image processing, analysis, and interpretation, pattern recognition, (deep) machine learning, and intelligent system design.

 

Master the processes of indexing and semantic processing of digital content

→ Train professionals capable of exploiting and automating the analysis of content and information in multimedia and cross-media databases.

 

Design and implement innovative solutions in applied fields

→ Apply theoretical methods to real-life cases such as medical imaging, remote sensing, video and document analysis, 3D imaging, or non-destructive (quality) testing.

 

Training in research and development (R&D) in the field of vision and imaging

→ Prepare students to contribute to scientific and technological innovation, both in academic laboratories and in companies in the sector.

 

The concepts of business intelligence and strategic intelligence for corporate defense will also be emphasized to raise students’ awareness of the need to protect information assets.

 

Career opportunities

The VMI track trains experts capable of designing, developing, and deploying high-performance, autonomous intelligent systems. Graduates acquire advanced skills in machine learning, image processing, multimedia data analysis, computer vision, artificial intelligence (AI), and software programming, opening them access to a wide range of careers.

 

Career opportunities are primarily in the technology, healthcare, manufacturing, transportation, electronic publishing, security/biometrics, and research sectors. Some of the most common roles include:

  • Computer Vision Engineer/Designer: Designing algorithms for object detection, recognition, and tracking;
  • Data Scientist/Data Engineer: Analyzing and leveraging (big) multimedia data for decision-making;
  • AI Researcher, Project Manager, or Engineer: Developing deep learning and generative AI models;
  • Intelligent Robotics Engineer: Integrating visual perception and decision-making into autonomous robots;
  • Intelligent Embedded Systems Developer: Designing AI solutions adapted to hardware constraints;
  • Innovation or Digital Transformation Consultant: Supporting companies in the integration of AI.

Graduates can work in technology companies, research laboratories, innovative startups, or industrial R&D centers, or even pursue a PhD in the LIPADE laboratory’s areas of expertise (or other organizations) to further their research in AI and computer vision.

 

Application process

You can find everything you need to know on the application webpage.

 

Course content

The first year (M1) is a core curriculum offering significant cross-curricular sharing with other Master’s in Computer Science programs. It introduces, among other things, the fundamentals of image processing, computational geometry, machine learning for vision, and pattern recognition. Its goal is to provide a set of computational and mathematical tools necessary for the program, as well as some opportunities.

 

The second year (M2) is inherently multidisciplinary, and students acquire the fundamental aspects of computer vision, integrating AI concepts to model systems ranging from data acquisition to knowledge extraction and interpretation (knowledge engineering), including generative models and multimodality. The concepts of business intelligence and strategic intelligence for corporate defense are also particularly emphasized to raise awareness of the protection of information assets. Students will be guided towards a career through their M2 internship, which takes place throughout the second semester (or through work-study placements throughout the year in FA) and can be carried out in a research laboratory or in a company, with the emphasis placed either on research or on development.

M1 semestre 1 (Informatique - Parcours VMI)
  • Programmation avancée – 6 ECTS (Cours : 15h, TD/TP : 25h)
  • Bases du traitement du signal et des images – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Introduction à la reconnaissance des formes – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Représentation des connaissances et raisonnement – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Optimisation et algorithmique – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Complexité algorithmique – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Probabilités et statistiques pour l’ingénieur – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Anglais – 3 ECTS (Autre forme d’enseignement : 20h)
  • 1 UE obligatoire au choix – 3 ECTS : Administration système Unix/Linux (Cours : 13h, TD/TP : 12h) ; Engagement étudiant ; Sport ; UE d’ouverture ; UE libre : mobilité
M1 semestre 2 (Informatique - Parcours VMI)
  • Big data – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Apprentissage machine – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Géométrie algorithmique – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Analyse d’images – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Réseaux de neurones pour la vision par ordinateur – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Data science – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Programmation distribuée – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Programmation web – 3 ECTS (Cours : 13h, TD/TP : 12h)
  • Projet tutoré VMI – 6 ECTS (Projet long étudiant)
  • Stage facultatif – 0 ECTS
M2 semestre 3 (Informatique - Parcours VMI)

Bloc “Vision par ordinateur” – 6 ECTS

  • Apprentissage profond pour la vision (Cours et TD/TP intégrés : 15h)
  • Imagerie 3D (Cours et TD/TP intégrés : 15h)
  • Imagerie biomédicale (Cours et TD/TP intégrés : 15h)
  • Séquences vidéo (Cours et TD/TP intégrés : 15h)

Bloc “Machine intelligente et perception” – 9 ECTS

  • Multi-modalité et IA générative (Cours et TD/TP intégrés : 15h)
  • Reconnaissance des formes avancée (Cours et TD/TP intégrés : 15h)
  • Méthodologie et imageries thématiques (Cours et TD/TP intégrés : 15h)
  • Recherche et extraction sémantique à partir de texte (Cours et TD/TP intégrés : 15h)
  • Interaction humain-robot (Cours et TD/TP intégrés : 15h)

Bloc “Veille et sécurité de l’information” – 6 ECTS

  • Gouvernance et sécurité de l’information (Cours et TD/TP intégrés : 12h)
  • Sécurité de l’information appliquée (Cours et TD/TP intégrés : 12h)
  • Veille et recherche sur sources ouvertes (Cours et TD/TP intégrés : 12h)

Bloc “Compétences transversales” – 9 ECTS

  • Méthodes d’évaluation de la recherche (Cours et TD/TP intégrés : 15h)
  • Modélisation de systèmes intelligents (Projet long étudiant d’initiation à la recherche)
  • Projet de vision industrielle (TD/TP : 35h)
M2 semestre 4 (Informatique - Parcours VMI)
  • Stage de M2 VMI – 30 ECTS (Stage de 4 à 6 mois)